Feature detection from local energy
Pattern Recognition Letters
Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous dimensionality characterization of image structures
Image and Vision Computing
IEEE Transactions on Signal Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
The 2D analytic signal on RF and B-mode ultrasound images
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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The (semi-) automatic detection of significant (feature) points is a key task in in vivo assessment of cancer staging and progression. However, this is a challenging task due to the relatively poor signal-to-noise, limited resolution and variable intensity of medical images. We propose to use phase congruence (PC), the Morrone and Owens (1987) feature model, to extract local descriptors. We overcome the limitations of the currently accepted PC measure, estimate PC without using an image energy weighting factor. We show that: (i) relative phase values from a single scale are not equivalent to phase values from PC, and should not be used to assess local image structure; and (ii) our approach results in higher specificity to features of interest, and lower sensitivity to noise, as demonstrated in in vitro microscopy (e.g. tumour microvessels) and in vivo pre-clinical pancreatic cancer images.